scholarly journals On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Fábio Henrique M. Oliveira ◽  
Alessandro R. P. Machado ◽  
Adriano O. Andrade
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Visualization ◽  
Motor Behavior ◽  
Neurodegenerative Disorder ◽  
Learning Algorithm ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Healthy Individuals ◽  
Sammon’S Mapping

Parkinson’s disease (PD) is a neurodegenerative disorder that remains incurable. The available treatments for the disorder include pharmacologic therapies and deep brain stimulation (DBS). These approaches may cause distinct side effects and motor responses. This work presents the application of t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for nonlinear dimensionality reduction and data visualization, for the problem of discriminating neurologically healthy individuals from those suffering from PD (treated with levodopa and DBS). Furthermore, the assessment of classification methods is presented. Inertial and electromyographic data were collected while the subjects executed a sequence of four motor tasks. The results were focused on the comparison of the classification performance of a support vector machine (SVM) while discriminating two-dimensional feature sets estimated from Principal Component Analysis (PCA), Sammon’s mapping, and t-SNE. The results showed visual and statistical differences for all three investigated groups. Classification accuracy for PCA, Sammon’s mapping, and t-SNE was, respectively, 73.5%, 78.6%, and 96.9% for the training set and 67.8%, 74.1%, and 76.6% for the test set. The possibility of discriminating healthy individuals from those with PD treated with levodopa and DBS highlights the fact that each treatment method produces distinct motor behavior. The scatter plots resulting from t-SNE could be used in the clinical practice as an objective tool for measuring the discrepancy between normal and abnormal motor behaviors, being thus useful for the adjustment of treatments and the follow-up of the disorder.

scholarly journals Neuroprotective Effect of Curcumin on the Nigrostriatal Pathway in a 6-Hydroxydopmine-Induced Rat Model of Parkinson’s Disease is Mediated by α7-Nicotinic Receptors

2020 ◽  
Vol 21 (19) ◽  
pp. 7329
Author(s):  
Eslam El Nebrisi ◽  
Hayate Javed ◽  
Shreesh K Ojha ◽  
Murat Oz ◽  
Safa Shehab
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rat Model ◽  
Dopaminergic Neurons ◽  
Motor Behavior ◽  
Neurodegenerative Disorder ◽  
Neuroprotective Effect ◽  
Intraperitoneal Administration ◽  
Neuroprotective Effects ◽  
Α7 Nachr

Parkinson’s disease (PD) is a common neurodegenerative disorder, characterized by selective degeneration of dopaminergic nigrostriatal neurons. Most of the existing pharmacological approaches in PD consider replenishing striatal dopamine. It has been reported that activation of the cholinergic system has neuroprotective effects on dopaminergic neurons, and human α7-nicotinic acetylcholine receptor (α7-nAChR) stimulation may offer a potential therapeutic approach in PD. Our recent in-vitro studies demonstrated that curcumin causes significant potentiation of the function of α7-nAChRs expressed in Xenopus oocytes. In this study, we conducted in vivo experiments to assess the role of the α7-nAChR on the protective effects of curcumin in an animal model of PD. Intra-striatal injection of 6-hydroxydopmine (6-OHDA) was used to induce Parkinsonism in rats. Our results demonstrated that intragastric curcumin treatment (200 mg/kg) significantly improved the abnormal motor behavior and offered neuroprotection against the reduction of dopaminergic neurons, as determined by tyrosine hydroxylase (TH) immunoreactivity in the substantia nigra and caudoputamen. The intraperitoneal administration of the α7-nAChR-selective antagonist methyllycaconitine (1 µg/kg) reversed the neuroprotective effects of curcumin in terms of both animal behavior and TH immunoreactivity. In conclusion, this study demonstrates that curcumin has a neuroprotective effect in a 6-hydroxydopmine (6-OHDA) rat model of PD via an α7-nAChR-mediated mechanism.

scholarly journals Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables

2021 ◽  
pp. 1-11
Author(s):  
Ibrahim Karabayir ◽  
Liam Butler ◽  
Samuel M. Goldman ◽  
Rishikesan Kamaleswaran ◽  
Fatma Gunturkun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Lewy Pathology ◽  
Non Invasive ◽  
Neuron Density ◽  
Light Gradient

Background: Parkinson’s disease (PD) is a chronic, disabling neurodegenerative disorder. Objective: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. Methods: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995–2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density. Results: The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76–0.89) or 5 years (AUC 0.77, 95%CI 0.71–0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = –0.57, p <  0.01). Conclusion: Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.

Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study

2021 ◽  
Author(s):  
Chetan Balaji ◽  
D. S. Suresh
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Input Space

The aging population is primarily affected by Alzheimer’s disease (AD) that is an incurable neurodegenerative disorder. There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage. Various techniques are used to diagnose AD. EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis. Parkinson’s disease (PD) is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year. The voice features are majorly used as the main means to diagnose PD. The major symptoms of PD are loss of intensity, the monotony of loudness and pitch, reduction in stress, unidentified silences, and dysphonia. Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classification diseases, still there is a need for efficient learning methodologies and techniques. This paper provides a review on using machine learning (ML) together with several feature extraction techniques that is helpful in the early detection of AD with Parkinson’s. The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD. The proposed technique depends on the nonlinear process for data dimension reduction, feature removal, and classification using kernel-based support vector machine (SVM) classifiers. The dimension of the input space is radically diminished with kernel methods. As the learning set is labeled, it creates sense to utilize this information to make a dependable method of dropping the input space dimension. The different techniques of ML are explained under the major approaches viz. SVM, artificial neural network (ANN), deep learning (DL), and ensemble methods. A comprehensive assessment is presented at SVM, ANN, and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.

scholarly journals MicroRNA Candidate Biomarkers for Parkinson’s Disease and Idiopathic REM Sleep Behavior Disorder

2021 ◽  
Author(s):  
Ligang Wu ◽  
Jun Liu ◽  
Yuanyuan Li ◽  
Ying Cao ◽  
Wei Liu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rem Sleep ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Individuals ◽  
Mirna Signature ◽  
Sleep Behavior

Abstract Background: Parkinson’s disease (PD), a severe neurodegenerative disorder, and idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD), a parasomnia recognized as the prodromal stage of synucleinopathies (including PD), both lack reliable, non-invasive biomarker tests for early intervention and management. Objectives: To investigate whether plasma extracellular vesicle (EV)-associated sncRNAs could discriminate PD and/or iRBD patients from healthy individuals.Methods: We optimized a cDNA library construction method, EVsmall-seq, for high throughput sequencing of sncRNAs associated with plasma EVs. We profiled EV-sncRNAs from the plasma of 60 normal controls, 56 iRBD patients, and 53 PD patients, and constructed a support vector machine (SVM) classifier to identify the informative miRNA features to distinguish PD and/or iRBD patients from healthy individuals. Results: First, a sixteen-miRNA signature was found to distinguish PD patients from healthy individuals with 88% sensitivity, 90.43% specificity, and 89.13% accuracy. Second, a three-miRNA signature was found to distinguish iRBD patients from healthy individuals with 96% sensitivity, 86.36% specificity, and 91.49% accuracy. Third, tweenty 20 miRNAs were found consistently increased or decreased in expression from healthy subjects to iRBD to PD patients, which might be linked to PD development through iRBD.Conclusions: Current study provides a valuable and highly informative dataset of EV-associated sncRNAs from plasma of iRBD and PD patients. We identified miRNA signature features that could serve as minimally-invasive, blood-based surveillance biomarkers for distinguishing iRBD or PD from healthy individuals with high sensitivity, specificity, and accuracy.

scholarly journals Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1076
Author(s):  
Muntasir Hoq ◽  
Mohammed Nazim Uddin ◽  
Seung-Bo Park
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Nearest Neighbor ◽  
Neurodegenerative Disorder ◽  
Imbalanced Data ◽  
Principal Component ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Extreme Gradient Boosting

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models.

Rapid Diagnosis of Parkinson’s Disease from Sebum using Paper Spray Ionisation Ion Mobility Mass Spectrometry

2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ion Mobility ◽  
Neurodegenerative Disorder ◽  
Treatment Options ◽  
Ion Mobility Mass Spectrometry ◽  
Paper Spray ◽  
Molecular Features ◽  
Validation Set

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.

Visual Effects of Deep Brain Stimulation in a Patient with Parkinson’s Disease

2019 ◽  
pp. 158-173
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Visual System ◽  
Brain Stimulation ◽  
Neurodegenerative Disorder ◽  
Initial Examination ◽  
Before And After ◽  
Deep Brain ◽  
Cover Test

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by a dopamine deficiency that presents with motor symptoms. Visual disorders can occur concomitantly but are frequently overlooked. Deep brain stimulation (DBS) has been an effective treatment to improve tremors, stiffness and overall mobility, but little is known about its effects on the visual system. Case Report: A 75-year-old Caucasian male with PD presented with longstanding binocular diplopia. On baseline examination, the best-corrected visual acuity was 20/25 in each eye. On observation, he had noticeable tremors with an unsteady gait. Distance alternating cover test showed exophoria with a right hyperphoria. Near alternating cover test revealed a significantly larger exophoria accompanied by a reduced near point of convergence. Additional testing with a 24-2 Humphrey visual field and optical coherence tomography (OCT) of the nerve and macula were unremarkable. The patient underwent DBS implantation five weeks after initial examination, and the device was activated four weeks thereafter. At follow up, the patient still complained of intermittent diplopia. There was no significant change in the manifest refraction or prism correction. On observation, the patient had remarkably improved tremors with a steady gait. All parameters measured were unchanged. The patient was evaluated again seven months after device activation. Although vergence ranges at all distances were improved, the patient was still symptomatic for intermittent diplopia. OCT scans of the optic nerve showed borderline but symmetric thinning in each eye. All other parameters measured were unchanged. Conclusion: The case found no significant changes on ophthalmic examination after DBS implantation and activation in a patient with PD. To the best of the authors’ knowledge, there are no other cases in the literature that investigated the effects of DBS on the visual system pathway in a patient with PD before and after DBS implantation and activation.

Peripheral Immunity, Immunoaging and Neuroinflammation in Parkinson’s Disease

2019 ◽  
Vol 26 (20) ◽  
pp. 3719-3753 ◽  
Author(s):  
Natasa Kustrimovic ◽  
Franca Marino ◽  
Marco Cosentino
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Neurodegenerative Disorder ◽  
Neurodegenerative Process ◽  
Progressive Degeneration ◽  
Cytokine Levels ◽  
Inflammatory Phenotype ◽  
Immune Challenges

:Parkinson’s disease (PD) is the second most common neurodegenerative disorder among elderly population, characterized by the progressive degeneration of dopaminergic neurons in the midbrain. To date, exact cause remains unknown and the mechanism of neurons death uncertain. It is typically considered as a disease of central nervous system (CNS). Nevertheless, numerous evidence has been accumulated in several past years testifying undoubtedly about the principal role of neuroinflammation in progression of PD. Neuroinflammation is mainly associated with presence of activated microglia in brain and elevated levels of cytokine levels in CNS. Nevertheless, active participation of immune system as well has been noted, such as, elevated levels of cytokine levels in blood, the presence of auto antibodies, and the infiltration of T cell in CNS. Moreover, infiltration and reactivation of those T cells could exacerbate neuroinflammation to greater neurotoxic levels. Hence, peripheral inflammation is able to prime microglia into pro-inflammatory phenotype, which can trigger stronger response in CNS further perpetuating the on-going neurodegenerative process.:In the present review, the interplay between neuroinflammation and the peripheral immune response in the pathobiology of PD will be discussed. First of all, an overview of regulation of microglial activation and neuroinflammation is summarized and discussed. Afterwards, we try to collectively analyze changes that occurs in peripheral immune system of PD patients, suggesting that these peripheral immune challenges can exacerbate the process of neuroinflammation and hence the symptoms of the disease. In the end, we summarize some of proposed immunotherapies for treatment of PD.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

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